Cannabidiol use and effectiveness: real-world evidence from a Canadian medical cannabis clinic
Bibliographic record
Abstract
BACKGROUND: Cannabidiol (CBD) is a primary component in the cannabis plant; however, in recent years, interest in CBD treatments has outpaced scientific research and regulatory advancement resulting in a confusing landscape of misinformation and unsubstantiated health claims. Within the limited results from randomized controlled trials, and lack of trust in product quality and known clinical guidelines and dosages, real-world evidence (RWE) from countries with robust regulatory frameworks may fill a critical need for patients and healthcare professionals. Despite growing evidence and interest, no real-world data (RWD) studies have yet investigated patients' reports of CBD impact on symptom control in the common expression of pain, anxiety, depression, and poor wellbeing. The objective of this study is to assess the impact of CBD-rich treatment on symptom burden, as measured with a specific symptom assessment scale (ESAS-r). METHODS: This retrospective observational study examined pain, anxiety, depression symptoms, and wellbeing in 279 participants over 18 years old, prescribed with CBD-rich treatment at a network of clinics dedicated to medical cannabis in Quebec, Canada. Data were collected at baseline, 3 (FUP1), and 6 (FUP2) month after treatment initiation. Groups were formed based on symptom severity (mild vs moderate/severe) and based on changes to treatment plan at FUP1 (CBD vs THC:CBD). Two-way mixed ANOVAs were used to assess ESAS-r scores differences between groups and between visits. RESULTS: All average ESAS-r scores decreased between baseline and FUP1 (all ps < 0.003). The addition of delta-9-tetrahydrocannabinol (THC) during the first follow-up had no effect on symptom changes. Patients with moderate/severe symptoms experienced important improvement at FUP1 (all ps < 0.001), whereas scores on pain, anxiety, and wellbeing of those with mild symptoms actually increased. Differences in ESAS-r scores between FUP1 and FUP2 were not statistically different. CONCLUSION: This retrospective observational study suggests CBD-rich treatment has a beneficial impact on pain, anxiety, and depression symptoms as well as overall wellbeing only for patients with moderate to severe symptoms; however, no observed effect on mild symptoms. The results of this study contribute to address the myths and misinformation about CBD treatment and demand further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".